Multi-Objective Optimization of an Aerodynamic Feeding System Using Genetic Algorithm
Considering the challenges of short product life cycles
and growing variant diversity, cost minimization and manufacturing
flexibility increasingly gain importance to maintain a competitive
edge in today’s global and dynamic markets. In this context, an
aerodynamic part feeding system for high-speed industrial assembly
applications has been developed at the Institute of Production
Systems and Logistics (IFA), Leibniz Universitaet Hannover. The
aerodynamic part feeding system outperforms conventional systems
with respect to its process safety, reliability, and operating speed. In
this paper, a multi-objective optimisation of the aerodynamic feeding
system regarding the orientation rate, the feeding velocity, and the
required nozzle pressure is presented.
[1] Konak, A.; Coit, D. W.; Smith, A. E. (2006): Multi-objective optimization using genetic algorithms. A tutorial. In: Reliability Engineering & System Safety 91 (9), S. 992–1007.
[2] Fonseca, C. M.; Fleming, P. J. (1998): Multiobjective optimization and multiple constraints handling with evolutionary algorithms. I. A unified formulation. In: IEEE Trans. Syst., Man, Cybern. A 28 (1), S. 26–37.
[3] Belyaev, A.; Maag, V.; Speckert, M.; Obermayr, M.; Küfer, K.-H. (2015): Multi-criteria optimization of test rig loading programs in fatigue life determination. In: Engineering Structures 101, S. 16–23.
[4] Gen, M.; Ida, K.; Li, Y.; Kubota, E. (1995): Solving bicriteria solid transportation problem with fuzzy numbers by a genetic algorithm. In: Computers & Industrial Engineering 29 (1-4), S. 537–541.
[5] Murata, T.; Ishibuchi, H.; Tanaka, H. (1996): Multi-objective genetic algorithm and its applications to flowshop scheduling. In: Computers & Industrial Engineering 30 (4), S. 957–968.
[6] Deb, K.; Jain, P.; Gupta, N. K.; Maji, H. K. (2004): Multiobjective Placement of Electronic Components Using Evolutionary Algorithms. In: IEEE Trans. Comp. Packag. Technol. 27 (3), S. 480–492.
[7] Kumar, R.; Parida, P. P.; Gupta, M.: Topological design of communication networks using multiobjective genetic optimization. In: 2002 World Congress on Computational Intelligence - WCCI'02. Honolulu, HI, USA, 12-17 May 2002, S. 425–430.
[8] Busch, J.; Quirico, M.; Richter, L.; Schmidt, M.; Raatz, A.; Nyhuis, P. (2015): A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly. In: CIRP Annals - Manufacturing Technology 64 (1), S. 5–8.
[9] Busch, J.; Knüppel, K. (2013): Development of a Self-Learning, Automatic Parameterisation of an Aerodynamic Part Feeding System. In: AMR 769, S. 34–41.
[10] Busch, J.; Schneider, S.; Knüppel, K.; Nyhuis, P. (2013): Identifying interactions in a feeding system. In: International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering 10, S. 931–937.
[1] Konak, A.; Coit, D. W.; Smith, A. E. (2006): Multi-objective optimization using genetic algorithms. A tutorial. In: Reliability Engineering & System Safety 91 (9), S. 992–1007.
[2] Fonseca, C. M.; Fleming, P. J. (1998): Multiobjective optimization and multiple constraints handling with evolutionary algorithms. I. A unified formulation. In: IEEE Trans. Syst., Man, Cybern. A 28 (1), S. 26–37.
[3] Belyaev, A.; Maag, V.; Speckert, M.; Obermayr, M.; Küfer, K.-H. (2015): Multi-criteria optimization of test rig loading programs in fatigue life determination. In: Engineering Structures 101, S. 16–23.
[4] Gen, M.; Ida, K.; Li, Y.; Kubota, E. (1995): Solving bicriteria solid transportation problem with fuzzy numbers by a genetic algorithm. In: Computers & Industrial Engineering 29 (1-4), S. 537–541.
[5] Murata, T.; Ishibuchi, H.; Tanaka, H. (1996): Multi-objective genetic algorithm and its applications to flowshop scheduling. In: Computers & Industrial Engineering 30 (4), S. 957–968.
[6] Deb, K.; Jain, P.; Gupta, N. K.; Maji, H. K. (2004): Multiobjective Placement of Electronic Components Using Evolutionary Algorithms. In: IEEE Trans. Comp. Packag. Technol. 27 (3), S. 480–492.
[7] Kumar, R.; Parida, P. P.; Gupta, M.: Topological design of communication networks using multiobjective genetic optimization. In: 2002 World Congress on Computational Intelligence - WCCI'02. Honolulu, HI, USA, 12-17 May 2002, S. 425–430.
[8] Busch, J.; Quirico, M.; Richter, L.; Schmidt, M.; Raatz, A.; Nyhuis, P. (2015): A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly. In: CIRP Annals - Manufacturing Technology 64 (1), S. 5–8.
[9] Busch, J.; Knüppel, K. (2013): Development of a Self-Learning, Automatic Parameterisation of an Aerodynamic Part Feeding System. In: AMR 769, S. 34–41.
[10] Busch, J.; Schneider, S.; Knüppel, K.; Nyhuis, P. (2013): Identifying interactions in a feeding system. In: International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering 10, S. 931–937.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:71555", author = "Jan Busch and Peter Nyhuis", title = "Multi-Objective Optimization of an Aerodynamic Feeding System Using Genetic Algorithm", abstract = "Considering the challenges of short product life cycles
and growing variant diversity, cost minimization and manufacturing
flexibility increasingly gain importance to maintain a competitive
edge in today’s global and dynamic markets. In this context, an
aerodynamic part feeding system for high-speed industrial assembly
applications has been developed at the Institute of Production
Systems and Logistics (IFA), Leibniz Universitaet Hannover. The
aerodynamic part feeding system outperforms conventional systems
with respect to its process safety, reliability, and operating speed. In
this paper, a multi-objective optimisation of the aerodynamic feeding
system regarding the orientation rate, the feeding velocity, and the
required nozzle pressure is presented.", keywords = "Aerodynamic feeding system, genetic algorithm,
multi-objective optimization.", volume = "9", number = "12", pages = "2092-8", }